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Topic: RBF NN traning data set limit
Replies: 0

 Greg Heath Posts: 6,387 Registered: 12/7/04
RBF NN traning data set limit
Posted: Mar 24, 2013 10:51 PM

Newsgroups: comp.ai.neural-nets
Subject: RBF NN traning data set limit
From: Sergio Ferlito <ferlito.ser...@gmail.com>
Date: Wed, 20 Mar 2013 03:59:42 -0700 (PDT)
Local: Wed, Mar 20 2013 6:59 am

On Mar 20, 6:59 am, Sergio Ferlito <ferlito.ser...@gmail.com> wrote:
> I'm new to RBF Neural Networks and I'm facing the following issue:
> in designing a rbf nn for forecasting a variable dependent by two inputs variables , if the traning data set excede 4000 samples, at least using Matlab newrbe function, I'm not able to get a result as Matlab stop responding and also hangs my computer (cpu utilization reaches 90% and memory utilization almost fill all available ram).
> I wonder if this is a limitation of RBF NN or a problem to Matlab implementation of RBF NN or ............probably I have committed a design error.
> Thanks.

1. Why didn't you crosspost to the MATLAB NEWSGROUP or
2. You might consider NEWRB instead of NEWRBE
3. It is doubtfull that you need 4000 training pairs
a. Standardize (zero-mean/unit-variance) x1 and x2
b. Plot y vs x1, y vs x2 and y vs (x1,x2)
c. How many points do you really need to adequately represent
both I-O relationships?
4. Randomly partition the data into training, validation and test subsets
Typically, Ntrn is large enough to adequately represent the plots in
1b and Ntst = Nval. An initial split might be (1334,1333,1333) or
(2666,667,667) and, if unsuccessful, changed.
5. Loop
a. Create a lot of nets for a full range of Gaussian half-widths.
b. Rank the nets using the validation set and choose a "best" net.
c. Evaluate the best net using the test set.
d. If unsuccessful, choose another random partition and repeat.

P.S. Some people CHEAT: They do not use a test set and choose the
best net obtained from the validation set. However, since the
validation set is twice as big as before, I'm not sure if the bias